Customizing Plots

scales, labels, facet_wrap()

library(tidyverse)

#Import the can_lang dataset 
can_lang <- read.csv("https://raw.githubusercontent.com/ttimbers/canlang/master/inst/extdata/can_lang.csv")

A starting graph: scatterplot of can_lang

can_lang_plot <- ggplot(can_lang, aes(x=most_at_home, y=mother_tongue)) +
  geom_point() + 
  xlab("Language spoken most at home \n (number of Canadian residents)") +
  ylab("Mother tongue \n (number of Canadian residents)") 

Notice anything weird about this plot?

Axis display format: scales package

 # Install the package if needed
library(scales)

We want to customize how the continuous x and y axes look, so we need to use the argument labels=label_comma() inside a scale_*_continuous() layer:

can_lang_plot 

What other formats are available in the scales package?

When passing a formatting function inside scale_*_continuous(labels = ...) you have options!

Function	Use Case	Example Input	Example Output
label_comma()	Formats numbers with commas	1234567	"1,234,567"
label_dollar()	Formats numbers as dollar currency	99.99	"$99.99"
label_dollar(prefix = "€")	Formats numbers as euro currency	99.99	"99.99€"
label_percent()	Converts decimals to percent	0.25	"25%"
label_pvalue()	Formats p-values	0.00005	"<0.0001"

Anything else?

Logarithmic Axes Transformations

Applying a Log Transformation

When you apply a log transformation to an axis (or both axes) in a plot, you convert values using a logarithmic scale instead of a linear scale. This means:

• Instead of evenly spaced values (1, 2, 3, 4, …), a logarithmic scale spaces values exponentially (1, 10, 100, 1000, …).
• The distance between ticks represents a multiplicative factor instead of an additive one.

See how much more clearly we can see all the points!

For you to do this yourself, you need to use scale_*_log10() instead of scale_*_continuous():

can_lang_plot 

1. converts x-axis to a log-scale
2. converts y-axis to a log-scale

Use ✅ scale_*_log10() instead of 🚫log(variable)

Using percents on a log scale

mutate to create new columns with percentage of Canadians who speak the language as their mother tongue:

can_lang <- can_lang %>%
  mutate(
    mother_tongue_percent = (mother_tongue / 35151728) * 100,
    most_at_home_percent = (most_at_home / 35151728) * 100
  )

Scatterplot with Percents and Colors

Create a scatterplot with most_at_home_percent and mother_tongue_percent. Vary the color and shape of the points depending on the category of language. You may need to adjust the position of the legend:

can_lang_percent_plot <- ggplot(can_lang, aes(x = most_at_home,  
                     y = mother_tongue )) +  
  geom_point(aes(color = category, shape=category), alpha=0.5) +
  xlab("Language spoken most at home \n (percentage of Canadian residents)") +
  ylab("Mother tongue \n (percentage of Canadian residents)") +
  theme(legend.position = "top", legend.direction = "vertical") +
  scale_x_log10(labels = comma) +
  scale_y_log10(labels = comma)

can_lang_percent_plot 

Labels

Adding text to a plot is one of the most common forms of annotation. Most plots will not benefit from adding text to every single observation on the plot, but labeling outliers and other important points is very useful.

A add label for each language in this dataset using geom_text(aes(label = language)):

can_lang_percent_plot  

Yikes! This is way too much going on in one plot. A few options to try when this happens:

• Decrease the font size of the labels (using the size= argument inside geom_text).
• Use the ggrepel package to spread out the labels a bit more
• Pick out only a subset of the points to label

Using ggrepel

Artwork by @allisonhorst

library(ggrepel)

can_lang_percent_plot 

Subset the labels

Create a new column for the labels. Use case_when (or ifelse) to only use the official language names and not to put a label for other language categories.

can_lang <- can_lang %>% 
  mutate(official_languages = case_when(category == "Official languages" ~ language, TRUE ~ NA ))

can_lang_percent_plot 

# We need to redo the base plot with the new can_lang dataset with the new official_languages column in it 
can_lang_percent_plot <- ggplot(can_lang, aes(x = most_at_home_percent,  y = mother_tongue_percent)) +
  geom_point(aes(color = category, shape=category)) +
  xlab("Language spoken most at home \n (percentage of Canadian residents)") +
  ylab("Mother tongue \n (percentage of Canadian residents)") +
  theme(legend.position = "top", legend.direction = "vertical") + 
  scale_x_log10(labels = comma) +
  scale_y_log10(labels = comma)

Facet Wrap

facet_wrap() is a function in the ggplot2 package that allows you to create a multi-panel plot showing a similar plot over different subsets of the data, usually different values of a categorical variable.

Create separate side-by-side plots for each different category of language.

can_lang_percent_plot